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A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8
Open AccessArticle

Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening

School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech & Image Information Processing, Northwestern Polytechnical University, Xi’an 710129, China
Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, School of Physics and Electronic Information, Yan’an University, Yan’an 716000, China
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2608;
Received: 13 October 2019 / Revised: 30 October 2019 / Accepted: 4 November 2019 / Published: 7 November 2019
(This article belongs to the Section Remote Sensing Image Processing)
In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening). However, the small-scale data and the gradient vanishing problem have been preventing the existing CNN-based fusion approaches from leveraging deeper networks that potentially have better representation ability to characterize the complex nonlinear mapping relationship between the input (source) and the targeting (fused) images. In this paper, we introduce a very deep network with dense blocks and residual learning to tackle these problems. The proposed network takes advantage of dense connections in dense blocks that have connections for arbitrarily two convolution layers to facilitate gradient flow and implicit deep supervision during training. In addition, reusing feature maps can reduce the number of parameters, which is helpful for reducing overfitting that resulted from small-scale data. Residual learning is explored to reduce the difficulty for the model to generate the MS image with high spatial resolution. The proposed network is evaluated via experiments on three datasets, achieving competitive or superior performance, e.g. the spectral angle mapper (SAM) is decreased over 10% on GaoFen-2, when compared with other state-of-the-art methods. View Full-Text
Keywords: multispectral pansharpening; images fusion; dense block; residual learning; CNNs multispectral pansharpening; images fusion; dense block; residual learning; CNNs
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Wang, D.; Li, Y.; Ma, L.; Bai, Z.; Chan, J.C.-W. Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening. Remote Sens. 2019, 11, 2608.

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